Massive multiple input, multiple output (MIMO) is a candidate technology for 5G wireless networks. In contrast to base stations for conventional MIMO systems, base stations in massive MIMO systems are equipped with many more antennas (e.g., approximately 20 to 100 antennas or more in next generation systems). In massive MIMO, a larger number of users are served simultaneously using multiuser MIMO techniques. In massive MIMO, thermal noise and fast fading vanish. Massive MIMO also provides simplified multiuser processing, reduced transmit power, and high sum-rates.
Channel state information (CSI) is an important parameter in massive MIMO systems. The CSI is used on the uplink to separate users through receive beamforming and is used on the downlink to send different data to different users through transmit beamforming.
Prior work on massive MIMO often assumes time division duplexing (TDD). Due to channel reciprocity, forward and reverse link channels are the same in TDD. The base station estimates channels based on uplink pilots. Due to channel reciprocity, explicit channel state information (CSI) feedback is not required. However, uplink channel estimates may be contaminated by pilot reuse in neighboring cells. Furthermore, non-ideal hardware and calibration error cause additional channel estimation errors.
Although the prior work on massive MIMO often assumes TDD mode, it would be desirable to have a massive MIMO with the benefits of FDD mode. FDD is a common duplexing strategy in current wireless systems. Therefore, upgrading to a FDD based massive MIMO system may be desirable. However, in FDD mode, channel reciprocity no longer holds due to different carrier frequencies on the uplink (UL) and the downlink (DL). DL training is required for the user to estimate downlink CSI for coherent detection. Furthermore, all users often need to send an estimate of the downlink CSI to the base station for precoding design in what is usually called feedback. However, this feedback consumes valuable system resources.